This invention relates generally to intelligent electronics devices (IEDs), e.g., electronic trip units or protective relays. More specifically, the present invention relates a method of event analysis of an electrical distribution system at an electronic trip unit or protective relay, i.e., an intelligent electronic device.
Intelligent electronic devices are well known. By way of example, an electronic trip unit (one such intelligent electronic device) typically comprises voltage and current sensors which provide analog signals indicative of the power line signals. The analog signals are converted by an A/D (analog/digital) converter to digital signals which are processed by a microprocessor. The trip unit further includes RAM (random access memory), ROM (read only memory) and EEPROM (electronic erasable programmable read only memory) all of which interface with the microprocessor. The ROM includes trip unit application code, e.g., main functionality firmware, including initializing parameters, and boot code. The EEPROM includes operational parameters for the application code.
Many electronic trip units (as well as other intelligent electronic devices) support event based waveform capture. That is, a cyclical sampling buffer constantly storing the real time digitized data of per-phase currents and voltages. Based on a user defined event trigger condition or a pre-defined event condition the buffer is frozen to preserve a representation of several cycles before and after the occurrence of an event. The data in the buffer allows for the subsequent analysis of the event.
However, users often express confusion regarding how to interpret this data. Voltage and current fluctuations can occur for a variety of different reasons, e.g., lightening strikes, large loads being added to or removed from the system, fault conditions that may have occurred or been cleared from the system. With just the raw data of the captured waveform to analyze, it is difficult to determine the source or potential impact of such a fluctuation. Additionally, it is also difficult to determine from the raw data if the frequency, duration, and magnitude of the fluctuations will affect the performance and/or the integrity of equipment powered by the electrical system. Severe fluctuations may damage connected loads.
Information Technology Industry Council (ITI), formerly the Computer Business Equipment Manufactures Association (CBEMA), defined an ITI (CBEMA) Curve as a guideline or standard. This Curve describes an a.c. voltage input boundary which typically can be tolerated by most Information Technology Equipment (ITE), e.g., computer controlled manufacturing equipment, computers, printers, copiers, facsimile machines, etc. The Curve is directed to both steady-state and transitory conditions.
It is therefore seen to be desirable to analyze characteristics of an electrical distribution system relative to a standard. This is done from within an intelligent electronic device. Briefly, in accordance with one embodiment of the present invention the ITI Curve as a standard to observed voltage data is used for assessing operating characteristics of an electrical distribution system, and to determine if operating conditions are safe for power equipment and characterize power anomalies accordingly.
A method of event analysis of an electrical distribution system at an intelligent electronic device (e.g., an electronic trip unit or protective relay) is presented. An electronic trip unit is described herein by way of an exemplary embodiment only, as the present invention applies to other intelligent electronic devices as well. The electronic trip unit comprising voltage and current sensors which provide analog signals indicative of the power line signals. The analog signals are converted by an A/D (analog/digital) converter to digital signals which are processed by a microprocessor. The trip unit further includes RAM (random access memory), ROM (read only memory) and EEPROM (electronic erasable programmable read only memory) all of which communicate with the microprocessor. The ROM includes trip unit application code, e.g., main functionality firmware, including initializing parameters, and boot code. The application code includes code for the event analysis algorithm of the present invention. The EEPROM includes operational parameters, e.g., code for setting the ITI Curve parameters for the application code. These parameters may be stored in the trip unit at the factory, but can also be remotely downloaded to incorporate updates and changes in the standard.
In an exemplary embodiment of the invention, the event analysis algorithm of an intelligent electronic device senses and quantifies data indicative of an event. The algorithm then determines the relationship of the quantified data to standards data indicative of a standards curve, and identifies the event based on this relationship to the curve.
In another embodiment of the invention, four quadrants are defined with respect to the ITI Curve, which substantially represent events corresponding to sags, surges, interruptions, or transients, as defined herein. Each event (data from a captured waveform) is then characterized as belonging to a safe or an unsafe region within each of the four quadrants. Thus, providing an analysis of the events.
The invention greatly reduces the confusion and time required to interpret the data from a captured waveform. Information relevant to the source of a current or voltage fluctuation can then be obtained from the location, frequency, duration, and magnitude of the fluctuation.
Additionally, the event analysis algorithm of the present invention is performed within the intelligent electronic devices itself. The analyzed and plotted information can either be read out on a display attached locally to the intelligent electronic device, or remotely to a central monitoring station. This is a significant advantage in large facilities containing many intelligent electronics devices capable of event based waveform capture that can be simultaneously affected by a single voltage fluctuation. The total volume of data required to be received and processed by the central monitoring station is greatly reduced. This represents a significant savings in data transmission time, processing time, computer processing capacity, and manual operator analysis.